Bayesian Texture Classification Based on Contourlet Transform and BYY Harmony Learning of Poisson Mixtures

As a newly developed 2-D extension of the wavelet transform using multiscale and directional filter banks, the contourlet transform can effectively capture the intrinsic geometric structures and smooth contours of a texture image that are the dominant features for texture classification. In this pap...

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Bibliographic Details
Published in:IEEE transactions on image processing 2012-03, Vol.21 (3), p.909-918
Main Authors: Dong, Yongsheng, Ma, Jinwen
Format: Article
Language:English
Subjects:
Adaptation models
Adaptive algorithms
Applied sciences
Bayesian analysis
Bayesian methods
Bayesian Ying-Yang (BYY) harmony learning system
Classification
contourlet transform
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Feature extraction
Hidden Markov models
Image processing
Information, signal and communications theory
Learning
model selection
Poisson mixtures
Shape
Signal and communications theory
Signal processing
Signal, noise
Surface layer
Telecommunications and information theory
Texture
texture classification
Training
Wavelet transforms
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Summary:As a newly developed 2-D extension of the wavelet transform using multiscale and directional filter banks, the contourlet transform can effectively capture the intrinsic geometric structures and smooth contours of a texture image that are the dominant features for texture classification. In this paper, we propose a novel Bayesian texture classifier based on the adaptive model-selection learning of Poisson mixtures on the contourlet features of texture images. The adaptive model-selection learning of Poisson mixtures is carried out by the recently established adaptive gradient Bayesian Ying-Yang harmony learning algorithm for Poisson mixtures. It is demonstrated by the experiments that our proposed Bayesian classifier significantly improves the texture classification accuracy in comparison with several current state-of-the-art texture classification approaches.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2011.2168231